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    <abstract>We investigate whether off-the-shelf deep bidirectional sentence representations (Devlin et al., 2019) trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser. This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages. In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task’s six truly low-resource languages while using a single system. However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the ‘universality’ of the whole approach.</abstract>
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%0 Conference Proceedings
%T Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations
%A Tran, Ke
%A Bisazza, Arianna
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F tran-bisazza-2019-zero
%X We investigate whether off-the-shelf deep bidirectional sentence representations (Devlin et al., 2019) trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser. This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages. In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task’s six truly low-resource languages while using a single system. However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the ‘universality’ of the whole approach.
%R 10.18653/v1/D19-6132
%U https://aclanthology.org/D19-6132
%U https://doi.org/10.18653/v1/D19-6132
%P 281-288

Markdown (Informal)

[Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://aclanthology.org/D19-6132) (Tran & Bisazza, 2019)

• Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (Tran & Bisazza, 2019)

ACL

• Ke Tran and Arianna Bisazza. 2019. Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 281–288, Hong Kong, China. Association for Computational Linguistics.